Abstract
This paper proposes a graph based Semi-Supervised Learning (SSL) approach by constructing a graph using a metric learning technique. It is important for SSL with a graph to calculate a good distance metric, which is crucial for many high-dimensional data sets, such as image classification. In this paper, we construct the similarity affinity matrix (graph) with the metric optimized by using Adaptive Metric Learning (AML) which performs clustering and distance metric learning simultaneously. Experimental results on real-world datasets show that the proposed algorithm is significantly better than graph based SSL algorithms in terms of classification accuracy, and AML gives a good distance metric to calculate the similarity of the graph. In eight benchmark datasets, 1 to 11 percent is attributed to the improvement of classification accuracy of state of the art graph based approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. MIT Press, Cambridge (2006)
Joachims, T.: Transductive inference for text classifcation using support vector machines. In: Proc. ICML (1999)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2434 (2006)
Wang, J., Chang, S., Zhou, X., Wong, S.: Active microscopic cellular image annotation by superposable graph transduction with imbalanced labels. In: Proc. IEEE Conference on CVPR (2008)
Zhou, D., Bousquet, O., Lal, T., Weston, J., Schökopf, B.: Learning with local and global consistency. In: NIPS 2004, pp. 595–602. The MIT Press, Cambridge (2004)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proc. ICML, vol. 20, p. 912 (2003)
Liu, W., Chang, S.F.: Robust multi-class transductive learning with graphs. In: Proc. IEEE Conference on CVPR, pp. 381–388 (2009)
Ye, J., Zhao, Z., Liu, H.: Adaptive distance metric learning for clustering. In: Proc. IEEE Conference on CVPR (2007)
Zhu, X.: Semi-supervised learning literature survey. Technical report, Computer Science, University of Wisconsin-Madison (2006)
Globerson, A., Roweis, S.: Metric learning by collapsing classes. Advances in Neural Information Processing Systems 18, 451 (2006)
Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, pp. 513–520 (2004)
Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press, New York (1990)
Tenenbaum, J., Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319 (2000)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. SCIENCE 290, 2323–2326 (2000)
Zha, Z.J., Mei, T., Wang, M., Wang, Z., Hua, X.S.: In: Proc. International Joint Conferences on Artificial Intelligence (IJCAI)
Dhillon, I., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of ACM SIGKDD, p. 556. ACM, New York (2004)
Wagstaff, K., Cardie, C., Rogers, S., Schrodl, S.: Constrained k-means clustering with background knowledge. In: Proc. ICML, pp. 577–584 (2001)
Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: Semi-supervised learning by maximizing smoothness. J. of Mach. Learn. Research (2004)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Okada, S., Nishida, T. (2010). Multi Class Semi-Supervised Classification with Graph Construction Based on Adaptive Metric Learning. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-15822-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
eBook Packages: Computer ScienceComputer Science (R0)